In mobile radio systems, the signals are propagated via two or more propagation paths between a transmitter and a receiver. The influence of this multipath propagation on the signal can be described in the form of a linear, time-variant transformation. The signal distortion which is caused by the multipath propagation makes it impossible to correctly detect the transmitted data without a correction mechanism. This correction mechanism, which is known as adaptive equalization and which can also be carried out in a RAKE receiver, is based on continuously repeated measurement of the channel characteristics of the transmission channel (channel estimation). The information about the transmission channel determined during the channel estimation process is used for equalization of the received signal.
In order to allow channel estimation in the receiver, the transmitter transmits symbols which are known in the receiver. These symbols which are known in the receiver are referred to as pilot or training symbols. The receiver receives the distorted pilot symbols which have been transmitted via the channel, and compares them with the transmitted pilot symbols. The channel coefficient which is applicable to the relevant propagation path at that time is then obtained from the comparison process which, for example, can be carried out by forming the quotient of the pilot symbols as received via a specific propagation path divided by the known pilot symbols.
With optimum channel knowledge, it is possible to compensate for the rotation and change in magnitude of the received complex-value symbol that occur in the transmission path. This allows the data to be detected with a lower bit error rate.
Various known algorithms are available for channel estimation. One known detection method which, however, may be used for channel estimation only in exceptional cases is matched filtering (MF), which requires no knowledge about the statistical characteristics of the channel and uses a maximum signal-to-interference-and-noise ratio as the optimality criterion. The known channel estimation methods, such as maximum likelihood estimation methods, use statistical characteristics of the channel. The Wiener filtering is one example of a channel estimation algorithm which takes account of statistical characteristics of the channel in the estimation process, as are described by a corresponding stochastic channel model. Wiener filtering minimizes the mean square estimation error MMSE (minimum mean square error) as the optimality criterion.
The channel estimation process is carried out as follows in practice. The sequence of transmitted complex pilot symbols for a single transmission path is denoted by p1, p2, . . . in the following text. The transmission channel multiplies the pilot symbol pk by the complex channel coefficient ck. This has additive noise nk superimposed on it, so that the symbol which is received via the propagation path under consideration has the form yk=pk*ck+nk, k=1, 2, . . . k is the index for the discrete time at the symbol clock rate. The channel estimation process is normally carried out in two steps. The first step comprises correlation of the received pilot symbol with the transmitted pilot symbol, for example in a calculation of the quotient zk=yk/pk. When there is no noise (nk=0), then zk=ck. The quotient zk can be regarded as unfiltered estimated value. However, the correlation process may also be carried out differently. In a second step, the sequence of unfiltered channel estimation values xk is filtered in order to produce final channel estimation values {tilde over (H)}(l).
FIGS. 1 and 2 show one known method for channel estimation for the transmission of spread-coded signals in a CDMA system.
Both so-called “Common Pilot” symbols (Common Pilots) and “Dedicated Pilot” symbols (Dedicated Pilots) are transmitted in the UMTS system in order to allow channel estimation. As is normal in the spread-coded transmission systems, these symbols are spread in the transmitter using a sequence which is known to the receiver. During the transmission process, the symbols then comprise SF chips whose duration is Tc, which is considerably shorter than the duration of the original data symbol Ts. The original data symbols or training symbols can be recovered by despreading in a receiver.
One such receiver (a RAKE receiver) is illustrated in FIG. 2. The RAKE receiver in FIG. 2, one designed in the normal way, has two or more RAKE fingers RF, whose outputs are passed to a combiner COM. A digital signal 1 is supplied to the input of the RAKE fingers RF, only one of which is illustrated schematically in FIG. 2, in a basic form by way of example, with this digital signal 1 having been produced in the normal manner (not illustrated) by down-mixing an antenna signal to an intermediate frequency band or to baseband and by sampling the down-mixed signal at a sufficiently high sampling frequency. The digital signal 1 is passed to a delay element DEL, whose task is to compensate for the path delay measured for a specific propagation path. A multiplier M1 for despreading the delay-compensated digital signal is located in the signal path downstream from the delay element DEL.
For this purpose, the signal that is emitted from the delay stage DEL is multiplied by a spread code PN (pseudo noise).
An integrate and dump unit INT is located in the signal path downstream from the despreading stage M1. The integrate and dump unit INT integrates a number of SF values (chips), and in the process produces one symbol. SF is the spreading factor of the CDMA (Code Division Multiple Access) channel under consideration.
The symbol sequence 2 which is emitted from the integrate and dump unit INT is passed to a further multiplier M2. The further multiplier M2 multiplies each symbol by an estimated channel coefficient {tilde over (H)}(l), which is passed to the multiplier M2 via a signal connection 3. As already mentioned, the output from the multiplier M2 is passed to the combiner COM. Based on the known functional principle of a RAKE receiver, the combiner COM combines the signal outputs from those RAKE fingers which demodulate those signal components of one and the same signal which are transmitted via different transmission paths. The signal which is produced at the output 4 of the combiner COM thus comprises signal contributions which have been obtained from two or more transmission paths. The process for signal combination as described above is, however, only one possible implementation. Real maximum ratio combining (MRC) may also be provided as an alternative to this.
The RAKE receiver is followed by a data detector, in a manner which is not illustrated. The simplest form of data detection is for a decision maker to compare each combined symbol value that is obtained with a threshold value (for example 0.5), and to use the comparison result to decide whether the symbol is a 0 (signal value ≦0.5) or a 1 (signal value ≧0.5).
The aspect which is important for the present invention relates to the calculation of the channel coefficients {tilde over (H)}(l). In order to calculate the channel coefficients {tilde over (H)}(l), the signal 2, whose symbol values are denoted by yk, is passed to a correlator KOR. The correlator KOR compares the received symbol values yk of pilot symbols with the pilot symbols which are known in the receiver. As already explained, this comparison can be carried out by forming the quotient zk=yk/pk of the received pilot symbols yk divided by the transmitted pilot symbols pk, which are known in the receiver. The channel estimation values z(1) are also referred to as unfiltered channel coefficients.
The unfiltered channel coefficients z(1) are passed via a data link 5 to a digital filter F. The digital filter F may be in the form of an FIR (finite impulse response) filter with a specific filter length, or an IIR (infinite impulse response) filter. The filter is designed on the basis of one of the optimality criteria which are known from statistical signal theory. The filter coefficients of the FIR filter are calculated on the basis of this optimality criterion, and are defined in a corresponding manner. For this purpose, the digital filter F has a control input 6, via which the filter coefficients of the digital filter F can be predetermined. The transfer function H(z) of the digital filter F is dependent on the filter coefficients which are supplied via the control input 6. The filtered channel coefficients {tilde over (H)}(l) are produced at the output of the digital filter F. A number of different sets of filter coefficients may be stored in the memory MEM.
As already mentioned, a number SF of the received chips are multiplied by the known spreading sequence in complex-conjugate form, and are integrated over the number SF of chips in order to produce a data symbol. In this case, the assumption is made that the channel to be estimated is approximately constant over the time period of SF symbols. This assumption is not as valid when SF is very large and/or there are high relative speeds between the mobile station and the base station as when SF is small and/or the relative speeds are low. The influence of the transmitted pilot symbols is then reversed in the correlator KOR, resulting in the complex vectors z(1). These values z(1) correspond to discrete sampling of the channel. The final channel estimation values {tilde over (H)}(l) are produced by filtering in the digital filter F. The best results are achieved by means of a digital filter based on variable estimation methods, which take account of the statistical parameters of the channel. One of these parameters is the variance of the noise which is interfering with the estimation symbols, and this is obtained from the signal-to-noise ratio (SNR) or the signal-to-interference-and-noise ratio (SINR) and the spreading factor (SF). This variance falls as the integration period increases, provided that the assumption of a quasi-stationary channel does not become invalid. A further important parameter is the dynamic range λ of the channel, which is expressed in the following manner by means of the Doppler frequency fd normalized with respect to the data symbol length Ts:λ=Ts×fd.
The narrower the channel dynamic range λ, the better is the channel estimation that can be achieved. However, very long filter lengths are required in order to achieve the potential estimation improvement when the values of λ are low. Filter sets which are optimized for nominal values λopt of the channel dynamic range based on λopt=Ts×fd,opt are normally used for channel estimation. Provided that the relationship λ≦λopt is valid, the estimation method can be used to achieve good results, in which case % opt should always be chosen to be as low as possible. The already mentioned Wiener filter is particularly highly suitable for the estimation methods described here, since it may be in the form of an FIR filter with variable coefficients. Moreover, in principle, the Wiener filter can also be optimized for the respective SINR. The sensitivity of the estimator to this parameter is low, however, provided that the SINR that is used for optimization is better than the actual SINR.
In the conventional method for channel estimation, two or more filter sets must be provided for the digital filter, which have been optimized for a specific number of possible speeds and SNR or SINR values. In this case, the sensitivity of the estimation result to the accuracy of the speed which is assumed for the estimation process is relatively high, that is to say as many filter sets as possible must be provided for different speeds, so that it is necessary to use a filter with variable coefficients. The major disadvantage of the known methods is that the length of the digital filter must be chosen to be very long in order also to actually achieve the theoretical estimation gains at low relative speeds. This long filter length is necessary in particular in order to reduce the influence of the noise on the estimated channel coefficient by means of adequate averaging.